Available methods of analysis for functional Magnetic Resonance Imaging offer a wealth of possibilities to researchers using this neuroimaging modality. However, these tools suffer from the inherent low signal to noise ratio of the data, and from the limitations of widely used model-based approaches. These problems have been addressed by the community and the literature now describes numerous methods that can remove part of the noise and extract brain activity pattern in a data-driven fashion. This project focuses on the design of optimized algorithms for the estimation and removal of the noise, on the understanding of the applicability of existing data-driven approaches, and on the development of new blind source separation methods for fMRI data. Particular attention will be given to quantification of the gains provided by the newly proposed methods by working on simulated datasets and specifically designed fMRI experiments. The first specific aim is to use a spatio-temporal four-dimensional multiresolution analysis to define an "'ideal denoising" scheme for a given study. It will make extensive use of the concept of best wavelet packet basis, which allows the most efficient representation of a signal. The concept wilt first be validated on fMRI rest datasets, and its efficiency will then be measured on simulated and actual data. The second specific aim focuses on blind source separation methods. An in depth study of Independent Component Analysis will be carried out to precisely define its field of applicability on fMRI data. By using sparsity together with time-frequency methods, we will develop new source separation algorithms and will demonstrate their robustness on both simulated and real data.